Kibela MCP Server

The mcp‑kibela server bridges AI assistants with Kibela, a collaborative knowledge‑base platform. By exposing a Model Context Protocol interface, it lets assistants such as Claude or Cursor query, retrieve, and manipulate Kibela content without exposing raw API keys or requiring custom integrations. This solves the common problem of “knowledge silos” where team documents live behind a proprietary web UI and are inaccessible to generative models that need up‑to‑date context.

At its core, the server offers a set of intuitive tools that map directly to Kibela’s REST API: searching notes, fetching the most recent entries, reading note content and comments by ID or path, and creating or updating notes. These capabilities are wrapped in MCP verbs that the client can invoke with simple JSON payloads, allowing developers to embed Kibela queries into conversational flows or automated workflows. The result is a seamless experience where an assistant can, for example, pull the latest sprint notes to answer a question or create a new task entry based on user input.

Key features include:

• Secure, token‑based access – the server requires only a team name and an API token, keeping credentials isolated from client code.
• Rich content handling – beyond plain text search, the server returns structured note data and comments, enabling assistants to surface contextually relevant excerpts.
• Bidirectional editing – create and update notes directly from the assistant, turning it into a collaborative editor that keeps team knowledge current.
• Path‑based retrieval – fetch notes by their Kibela path, which mirrors the folder structure users are familiar with.

Real‑world use cases abound: a project manager can ask an assistant to pull the latest design document and summarize it; a developer can trigger a new Kibela note from an issue tracker conversation; or a knowledge‑base bot can answer FAQs by querying the most recent policy updates. In each scenario, the assistant acts as a live connector to Kibela, reducing friction and ensuring that the information accessed is both authoritative and current.

Integration into AI workflows is straightforward. Clients such as Claude Desktop, Cursor, VSCode extensions, or the Smithery CLI can declare the mcp‑kibela server in their configuration. Once running, the assistant can call tools like or as part of a larger chain, using the server’s responses to inform subsequent reasoning steps. This tight coupling means developers can build sophisticated knowledge‑augmented applications without writing custom API wrappers, leveraging the MCP’s standardized request/response model instead.